def analize(XX, yy):
y_pred = regr.predict(XX)
# orig_y_true = scaler.inverse_transform(yy)
# orig_y_pred = scaler.inverse_transform(y_pred)
plot_result(yy, y_pred)
full_report(yy, y_pred)
def analzie(data_set):
## Select training example
y_true = data_set[:, -1, -1]
predictions = lstm_model.predict(data_set[:, :, :-1])
y_pred = predictions[:, -1, -1]
full_report(y_true, y_pred)
plot_result(y_true, y_pred)
def analzie(data_set, save=False):
## Select training example
y_true = data_set[:, -1, -1]
predictions = lstm_model.predict(data_set[:, :, :-1])
y_pred = predictions[:, -1, -1]
if save:
np.save('lstm', y_pred)
full_report(y_true, y_pred)
plot_result(y_true, y_pred)
def analzie(data_loader):
## Select training example
y_pred = []
y_true = []
for batch in data_loader:
x = batch[:, :, :-1]
y = batch[:, -1, -1]
with torch.no_grad():
netout = net(x)
true = y.cpu().numpy()
y_true.extend(true)
pred = netout[:, -1, -1]
pred = pred.cpu().numpy()
# print(pred)
y_pred.extend(pred)
y_true = np.array(y_true)
y_pred = np.array(y_pred)
# orig_y_true = scaler.inverse_transform(y_true)
# orig_y_pred = scaler.inverse_transform(y_pred)
full_report(y_true, y_pred)
plot_result(y_true, y_pred)
def analize(XX, yy):
y_pred = regr.predict(XX)
plot_result(yy, y_pred)
full_report(yy, y_pred)